Visualizing Differences

Visualization is meant to compare and contrast data, which lets you see patterns, glean insights, and all that. However, if we focus specifically on finding or displaying differences, some methods are more helpful than others. In this guide, I describe five ways to get this focus.

Straightforward Visualization

Visualize data like you normally would, without thinking specifically about differences. This is the “let the data speak” route, which comes with its own challenges, but allows readers to make their own conclusions (because it forces them to). Or, you can annotate to direct readers where to go, which is usually the best option for presentation graphics.

We see this with state-level data a lot too. Try to sort by a facet of the data that allows quick comparison. In the chart below, you can quickly see the differences in population makeup between the greatest populated state, the least, and everything in between.

Visual Encodings that Diverge

But hey, we’re talking about visualizing differences. If that’s what you’re after, it’s good to show the differences explicitly with visual encodings that diverge.

Maybe it’s a color scale that indicates a greater than, lesser than, and a neutral. Maybe the coordinate system uses a positive and negative side. Whatever it may be, encode the data in a way that visually divides the differences.

Alternatively, you could express the likelihood as a percentage of directions written for men, in which case the percentage of the directions for women would be implied. But that would focus on one sex. Some arithmetic allowed for equal visual weight in the straightforward bar chart.

Separate Categories

It’s tempting to squeeze all of your data into a single visualization. Sometimes a graphic looks more impressive with a lot of lines, points, and colors in one view. But it can also lead to a jumbled mess. If there’s too much going on, you won’t get anything out of the visual other than a pretty picture.

So when you have several people, places, or things, the visual might benefit from separation and categorization. Maybe that comes as small multiples. Maybe it comes as side-by-side comparisons.

Examples

Showing Only What’s Different

If it’s not necessary to show all of the data, which is a common occurrence, filter down to the points of interest, and then only show that. This might mean simply subsetting to a subpopulation, or it might mean a statistical clustering or identifier of some sort.

Either way, this is more of a statistical step than a design tip, but this is also why analysis and data graphics should tightly couple. Each informs the other.

Examples

When I looked for the the most trendy names in US history, I first classified names as trendy based on annual usage, and then only showed the most interesting bits.

I did the same when looking for most regional and most unisex names. (I had a kid on the way, so I was really into names at the time.) In all these examples, the statistical uniqueness of the names was the difference, and those that trended closer to the average were filtered out.

Animated Contrasts

Animation is sometimes a tricky beast, but when used with care, the method offers an intuitive view that shows readers how a units shift.

In the case of visualizing differences, a graphic can start in one phase or state and then the animation can show how it all moves to a different phase.

Examples

In visualizing income shifts, I used beeswarm plots to show distributions during different years. The initial view starts in 1960, and when a reader selects a different year, the dots move to show a new distribution.

As seen above, the more movement, the greater the difference is between the selected years.

Filters — Place differences in the foreground and place the rest far in the background.

Animation — Use movement as the visual cue.

Whatever method you choose, the key is to focus specifically on the differences. So instead of just visualizing your data, you also want to visualize aspects of your data, which in the end, are what matter most.

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